eMB: Bridging the Gap Between Agent Based Models of Complex Biological Phenomena and Real-World Data Using Surrogate Models

在过去三年里，我们作为个人、作为一个联合的国家和作为一个世界发生了许多事情。人类引起的变化对我们环境的影响以及不断变化的全球环境对人类的相互影响是深刻的。为了应对这些挑战并改善人类福祉，研究人员、科学家和工程师正在以新颖的多维形式生成大量关于我们世界及其居民不断变化的数据。不幸的是，现有的数学，统计和计算技术在理解这些复杂的数据集时只提供了部分易处理性。我们迫切需要新的、经过深思熟虑的数学方法和建模方法，以深入而有力地了解这些数据，从而为人类造福，并减轻对人类的伤害。该项目的成功完成将导致一个强大的和可扩展的计算框架，用于约束基于代理的模型与真实世界的数据的大参数空间。基于主体的模型被广泛认为是一个强大的计算框架，用于推进我们对人类疾病，人类社会相互作用和环境系统的理解。然而，它们固有的随机性和高昂的计算费用对将这些模型与真实世界的数据相结合构成了重大障碍。新方法将提供一个急需的平台，探索参数的不确定性和敏感性，在多尺度代理为基础的模型，代表复杂的生物现象。最终，这里开发的新方法将产生一种可扩展的数学工具，用于操作计算复杂的模型，这些模型旨在解决生物学家，生态学家，临床医生，和卫生政策制定者。释放复杂计算数学模型的全部潜力，以促进我们对相互关联的生物系统的理解，迫切需要将这些模型与多方面的真实世界数据。 多尺度基于代理的模型（ABM）被广泛认为是一个强大的计算框架，用于推进我们对从分子，细胞和组织动力学到人类社会相互作用，传染病和生态系统的系统的理解。 然而，为了进行有意义的、可靠的定量预测并获得机理性的见解，ABM必须通过模型参数化和校准与真实世界的数据相结合。不幸的是，强大的，可扩展的技术，以解决所带来的挑战，固有的随机性和沉重的计算要求的反弹道导弹在整合它与现实世界的数据是非常缺乏的。因此，迫切需要开发新的理论和计算方法，以弥合反弹道导弹参数和真实世界数据之间的差距。 该项目开发了一个新的计算框架，用于参数估计，不确定性量化和多尺度ABMs的灵敏度分析，这些数据来自嘈杂，稀疏和多方面的真实数据。 该方法利用明确制定和机械代理模型，同时推断出从ABM配方和数据连接在以前不可能的方式两者。该方法将为代表复杂生物现象的ABM开辟新的可能性，揭示数据集如何隐藏对预测结果和计划干预具有深远影响的意外或反直觉的潜在机制。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

Much has happened in the past three years - to us as individuals, as a united nation, and as one world. The consequences of human-induced transformations on our environment and the reciprocal impact of the changing global environment on humanity have been profound. To address these challenges and improve human well-being, researchers, scientists, and engineers are generating large amounts of data on the evolving condition of our world and its inhabitants in novel, multidimensional forms. Unfortunately, existing mathematical, statistical, and computational techniques offer only partial tractability in comprehending these complex datasets. New, thoughtfully developed mathematical methods and modeling approaches are desperately needed to gain a deep and robust understanding of these data for human benefit and to mitigate human harm. The successful completion of this project will result in a robust and scalable computational framework for constraining large parameter spaces in agent-based models with real-world data. Agent-based models are widely recognized as a powerful computational framework for advancing our understanding of human disease, human-society interactions, and environmental systems. However, their inherent stochasticity and prohibitive computational expense pose significant barriers to integrating such models with real-world data. The new approach will provide a much-needed platform for exploring parameter uncertainty and sensitivity in multiscale agent-based models representing complex biological phenomena. Ultimately, the new methods developed here will result in a scalable mathematical tool for operationalizing computationally complex models designed to solve formidable biological problems that are of great interest to biologists, ecologists, clinicians, and health policymakers.Unlocking the full potential of computationally complex mathematical models to advance our understanding of interconnected biological systems urgently requires techniques for integrating these models with multifaceted real-world data. Multiscale agent-based models (ABMs) are widely recognized as a powerful computation framework for advancing our understanding of systems ranging from molecular, cellular, and tissue dynamics to human-society interactions, infectious diseases, and ecological systems. However, to make meaningful, reliable quantitative predictions and to gain mechanistic insight, ABMs must be integrated with real-world data through model parameterization and calibration. Unfortunately, robust, scalable techniques for addressing the challenges posed by the inherent stochasticity and heavy computational requirements of an ABM in integrating it with real-world data are sorely lacking. Hence, there is a critical need to develop new theoretical and computational approaches to bridge this gap between ABM parameters and real-world data. This project develops a new computational framework for parameter estimation, uncertainty quantification, and sensitivity analysis of multiscale ABMs informed by noisy, sparse, and multifaceted real-world data. The method utilizes explicitly formulated and mechanistic surrogate models simultaneously inferred from both the ABM formulation and the data to link the two in previously impossible ways. The approach will open new possibilities for ABMs representing complex biological phenomena to uncover how data sets can hide unexpected or counter-intuitive underlying mechanisms that have profound implications for predicted outcomes and planned interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.